Abstract
Plant biomass is an important parameter for crop management and yield estimation. However, since biomass cannot be determined non-destructively, other plant parameters are used for estimations. In this study, plant height and hyperspectral data were used for barley biomass estimations with bivariate and multivariate models. During three consecutive growing seasons a terrestrial laser scanner was used to establish crop surface models for a pixel-wise calculation of plant height and manual measurements of plant height confirmed the results (R2 up to 0.98). Hyperspectral reflectance measurements were conducted with a field spectrometer and used for calculating six vegetation indices (VIs), which have been found to be related to biomass and LAI: GnyLi, NDVI, NRI, RDVI, REIP, and RGBVI. Furthermore, biomass samples were destructively taken on almost the same dates. Linear and exponential biomass regression models (BRMs) were established for evaluating plant height and VIs as estimators of fresh and dry biomass. Each BRM was established for the whole observed period and pre-anthesis, which is important for management decisions. Bivariate BRMs supported plant height as a strong estimator (R2 up to 0.85), whereas BRMs based on individual VIs showed varying performances (R2: 0.07–0.87). Fused approaches, where plant height and one VI were used for establishing multivariate BRMs, yielded improvements in some cases (R2 up to 0.89). Overall, this study reveals the potential of remotely-sensed plant parameters for estimations of barley biomass. Moreover, it is a first step towards the fusion of 3D spatial and spectral measurements for improving non-destructive biomass estimations.
Highlights
Over the past several decades remote sensing has increased in importance for precision agriculture [1,2,3].Since the world population is expected to increase by more than one third until 2050 a main goal is shrinking the gap between potential and current yield [4,5]
The overall aim of this study was to evaluate whether the fusion of plant height (PH) and vegetation indices (VIs) can improve the predictability of dry and fresh barley biomass compared to each parameter as individual estimator
The use of terrestrial laser scanning (TLS) to derive PH was verified and bivariate biomass regression models (BRMs) based on PH or one of six VIs as well as multivariate BRMs based on the fusion of PH with each VI were established
Summary
Over the past several decades remote sensing has increased in importance for precision agriculture [1,2,3]. Since the world population is expected to increase by more than one third until 2050 a main goal is shrinking the gap between potential and current yield [4,5]. Studies reveal that grain yield is correlated with total biomass [7,8]. A quantitative measure is the harvest index, which expresses yield vs total biomass [9]. In-season, the nitrogen nutrition index, the ratio between actual and critical nitrogen (N) content, is widely used as a measure of the plant status [11]. An exact in-season acquisition of biomass is important in precision agriculture
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